The idea of semantic segmentation is to recognize and understand what is in an image at the pixel-level.
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Our model shows significant improvements over state-of-the-art models across various visual recognition tasks, including image classification, object detection, and semantic segmentation.
#18 best model for Semantic Segmentation on Cityscapes
In this paper we attempt to overcome these limitations with the proposed architecture, by capturing richer contextual dependencies based on the use of guided self-attention mechanisms.
We also evaluate how conditioning the ground truths using different (but very simple) algorithms may help to enhance agreement and may be appropriate for some use cases.
By synthetic experiments, we further show the capability of our approach in learning an explicit anatomical shape representation.
The framework directly regresses 3D bounding boxes for all instances in a point cloud, while simultaneously predicting a point-level mask for each instance.
We therefore accept this task and propose an evaluation framework for predictive uncertainty estimation that is specifically designed to test the robustness required in real-world computer vision applications.
This paper conducts a systematic study on the role of visual attention in Unsupervised Video Object Segmentation (UVOS) tasks.
Recent success of semantic segmentation approaches on demanding road driving datasets has spurred interest in many related application fields.
The key observation is that, although the object is a 3D volume, what we really need in segmentation is to find its boundary which is a 2D surface.
Unlike previous work, we densely connect each point with every other in a local neighborhood, aiming to specify feature of each point based on the local region characteristics for better representing the region.